code: def _test_training(
      self, gpu, steps=5000, load_model=False, require_success=True
  ):
      random_seed.set_random_seed(1)
      logging.basicConfig(level=logging.DEBUG)
  
      env = self.make_env_and_successful_return(test=False)[0]
      test_env, successful_return = self.make_env_and_successful_return(
          test=True
      )
      agent = self.make_agent(env, gpu)
  
      if load_model:
          print("Load agent from", self.agent_dirname)
          agent.load(self.agent_dirname)
          agent.replay_buffer.load(self.rbuf_filename)
  
      # Train
      train_agent_with_evaluation(
          agent=agent,
          env=env,
          steps=steps,
          outdir=self.tmpdir,
          eval_interval=200,
          eval_n_steps=None,
          eval_n_episodes=5,
          successful_score=1,
          eval_env=test_env,
      )
  
      agent.stop_episode()
  
      # Test
      n_test_runs = 5
      for _ in range(n_test_runs):
          total_r = 0.0
          obs = test_env.reset()
          done = False
          reward = 0.0
          while not done:
              action = agent.act(obs)
              obs, reward, done, _ = test_env.step(action)
              total_r += reward
          agent.stop_episode()
          if require_success:
              self.assertAlmostEqual(total_r, successful_return)
  
      # Save
      agent.save(self.agent_dirname)
      agent.replay_buffer.save(self.rbuf_filename)
  
imports: import logging
  import os
  import tempfile
  import unittest
  from chainer import testing
  import numpy
  import chainerrl
  from chainerrl.experiments.evaluator import batch_run_evaluation_episodes
  from chainerrl.experiments import train_agent_batch_with_evaluation
  from chainerrl.experiments import train_agent_with_evaluation
  from chainerrl.misc import random_seed
label_desc: Use image node_id 4 for calling the _TestTraining obj's underlying member method code with example usage: obj._test_training(gpu, steps, load_model, require_success) without return types